Overview

Dataset statistics

Number of variables14
Number of observations177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.5 KiB
Average record size in memory112.7 B

Variable types

Categorical1
Numeric13

Alerts

Alcohol is highly overall correlated with Class and 2 other fieldsHigh correlation
Class is highly overall correlated with Alcohol and 6 other fieldsHigh correlation
Color intensity is highly overall correlated with AlcoholHigh correlation
Flavanoids is highly overall correlated with Class and 5 other fieldsHigh correlation
Hue is highly overall correlated with Class and 2 other fieldsHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Malic acid is highly overall correlated with HueHigh correlation
Nonflavanoid phenols is highly overall correlated with FlavanoidsHigh correlation
OD280/OD315 of diluted wines is highly overall correlated with Class and 3 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Class and 3 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 2 other fieldsHigh correlation
Total phenols is highly overall correlated with Class and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-07 05:33:09.860640
Analysis finished2023-12-07 05:33:21.881111
Duration12.02 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
2
71 
1
58 
3
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Length

2023-12-06T23:33:21.944633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T23:33:22.031641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring characters

ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.993672
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:22.128924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.658
Q112.36
median13.05
Q313.67
95-th percentile14.22
Maximum14.83
Range3.8
Interquartile range (IQR)1.31

Descriptive statistics

Standard deviation0.80880844
Coefficient of variation (CV)0.062246332
Kurtosis-0.84014629
Mean12.993672
Median Absolute Deviation (MAD)0.68
Skewness-0.046483486
Sum2299.88
Variance0.6541711
MonotonicityNot monotonic
2023-12-06T23:33:22.239085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 6
 
3.4%
12.37 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.3%
12.25 3
 
1.7%
12 3
 
1.7%
12.42 3
 
1.7%
12.51 2
 
1.1%
13.73 2
 
1.1%
13.58 2
 
1.1%
Other values (115) 141
79.7%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.22 2
1.1%
14.21 1
0.6%
14.2 1
0.6%

Malic acid
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.339887
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:22.348480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.058
Q11.6
median1.87
Q33.1
95-th percentile4.464
Maximum5.8
Range5.06
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1193144
Coefficient of variation (CV)0.47836259
Kurtosis0.27858088
Mean2.339887
Median Absolute Deviation (MAD)0.52
Skewness1.0309746
Sum414.16
Variance1.2528648
MonotonicityNot monotonic
2023-12-06T23:33:22.522246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
4.0%
1.67 4
 
2.3%
1.81 4
 
2.3%
1.9 3
 
1.7%
1.68 3
 
1.7%
1.53 3
 
1.7%
1.51 3
 
1.7%
1.35 3
 
1.7%
1.61 3
 
1.7%
1.5 2
 
1.1%
Other values (123) 142
80.2%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash
Real number (ℝ)

Distinct78
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3661582
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:22.687137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.56
95-th percentile2.742
Maximum3.23
Range1.87
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.27508044
Coefficient of variation (CV)0.11625615
Kurtosis1.1223747
Mean2.3661582
Median Absolute Deviation (MAD)0.16
Skewness-0.17240561
Sum418.81
Variance0.075669248
MonotonicityNot monotonic
2023-12-06T23:33:22.909644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 7
 
4.0%
2.3 7
 
4.0%
2.7 6
 
3.4%
2.32 6
 
3.4%
2.36 6
 
3.4%
2.2 5
 
2.8%
2.38 5
 
2.8%
2.48 5
 
2.8%
2.1 4
 
2.3%
2.4 4
 
2.3%
Other values (68) 122
68.9%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Alcalinity of ash
Real number (ℝ)

Distinct62
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.516949
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:23.050608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.76
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3360711
Coefficient of variation (CV)0.170932
Kurtosis0.50667253
Mean19.516949
Median Absolute Deviation (MAD)2
Skewness0.20407561
Sum3454.5
Variance11.12937
MonotonicityNot monotonic
2023-12-06T23:33:23.192557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.5%
16 11
 
6.2%
21 11
 
6.2%
18 10
 
5.6%
19 9
 
5.1%
21.5 8
 
4.5%
19.5 7
 
4.0%
18.5 7
 
4.0%
22.5 7
 
4.0%
22 7
 
4.0%
Other values (52) 85
48.0%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.587571
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:23.324069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.8
Q188
median98
Q3107
95-th percentile123.2
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.174018
Coefficient of variation (CV)0.14232718
Kurtosis2.2643344
Mean99.587571
Median Absolute Deviation (MAD)10
Skewness1.1221477
Sum17627
Variance200.9028
MonotonicityNot monotonic
2023-12-06T23:33:23.439192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.2%
98 9
 
5.1%
101 9
 
5.1%
96 8
 
4.5%
102 7
 
4.0%
112 6
 
3.4%
85 6
 
3.4%
94 6
 
3.4%
92 5
 
2.8%
Other values (42) 97
54.8%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.2%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
126 1
0.6%
124 1
0.6%
123 1
0.6%

Total phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2922599
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:23.552233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.74
median2.35
Q32.8
95-th percentile3.276
Maximum3.88
Range2.9
Interquartile range (IQR)1.06

Descriptive statistics

Standard deviation0.62646508
Coefficient of variation (CV)0.27329584
Kurtosis-0.83241828
Mean2.2922599
Median Absolute Deviation (MAD)0.51
Skewness0.097688265
Sum405.73
Variance0.3924585
MonotonicityNot monotonic
2023-12-06T23:33:23.676216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
3 6
 
3.4%
2.6 6
 
3.4%
2 5
 
2.8%
2.8 5
 
2.8%
2.95 5
 
2.8%
2.85 4
 
2.3%
2.45 4
 
2.3%
1.65 4
 
2.3%
1.38 4
 
2.3%
Other values (87) 126
71.2%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.3%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0234463
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:23.780468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.544
Q11.2
median2.13
Q32.86
95-th percentile3.5
Maximum5.08
Range4.74
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation0.99865762
Coefficient of variation (CV)0.49354292
Kurtosis-0.87216456
Mean2.0234463
Median Absolute Deviation (MAD)0.83
Skewness0.036879791
Sum358.15
Variance0.99731703
MonotonicityNot monotonic
2023-12-06T23:33:23.888753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.3%
2.03 3
 
1.7%
2.68 3
 
1.7%
0.58 3
 
1.7%
0.6 3
 
1.7%
1.25 3
 
1.7%
2.79 2
 
1.1%
1.09 2
 
1.1%
1.75 2
 
1.1%
1.69 2
 
1.1%
Other values (121) 150
84.7%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Nonflavanoid phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36231638
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:24.102502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.44
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.12465293
Coefficient of variation (CV)0.34404443
Kurtosis-0.64669127
Mean0.36231638
Median Absolute Deviation (MAD)0.09
Skewness0.44093698
Sum64.13
Variance0.015538354
MonotonicityNot monotonic
2023-12-06T23:33:24.205480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.26 11
 
6.2%
0.43 11
 
6.2%
0.29 10
 
5.6%
0.32 9
 
5.1%
0.27 8
 
4.5%
0.34 8
 
4.5%
0.4 8
 
4.5%
0.3 8
 
4.5%
0.37 8
 
4.5%
0.24 7
 
4.0%
Other values (29) 89
50.3%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
4.0%
0.25 2
 
1.1%
0.26 11
6.2%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.3%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
4.0%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5869492
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:24.303045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.55
Q31.95
95-th percentile2.712
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57154472
Coefficient of variation (CV)0.36015314
Kurtosis0.59786217
Mean1.5869492
Median Absolute Deviation (MAD)0.37
Skewness0.53278674
Sum280.89
Variance0.32666337
MonotonicityNot monotonic
2023-12-06T23:33:24.412053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.1%
1.46 7
 
4.0%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.66 4
 
2.3%
1.56 4
 
2.3%
2.08 4
 
2.3%
1.98 4
 
2.3%
1.77 3
 
1.7%
1.4 3
 
1.7%
Other values (91) 128
72.3%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0548023
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:24.519514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.112
Q13.21
median4.68
Q36.2
95-th percentile9.604
Maximum13
Range11.72
Interquartile range (IQR)2.99

Descriptive statistics

Standard deviation2.3244464
Coefficient of variation (CV)0.45984913
Kurtosis0.36993779
Mean5.0548023
Median Absolute Deviation (MAD)1.52
Skewness0.87085005
Sum894.7
Variance5.4030512
MonotonicityNot monotonic
2023-12-06T23:33:24.666864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 4
 
2.3%
2.6 4
 
2.3%
4.6 4
 
2.3%
3.4 3
 
1.7%
4.5 3
 
1.7%
5.4 3
 
1.7%
5.6 3
 
1.7%
5 3
 
1.7%
3.05 3
 
1.7%
5.7 3
 
1.7%
Other values (121) 144
81.4%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95698305
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:24.787917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.78
median0.96
Q31.12
95-th percentile1.286
Maximum1.71
Range1.23
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.22913505
Coefficient of variation (CV)0.2394348
Kurtosis-0.35507479
Mean0.95698305
Median Absolute Deviation (MAD)0.16
Skewness0.026963707
Sum169.386
Variance0.052502869
MonotonicityNot monotonic
2023-12-06T23:33:24.907462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 7
 
4.0%
1.23 7
 
4.0%
1.12 6
 
3.4%
0.57 5
 
2.8%
0.89 5
 
2.8%
0.96 5
 
2.8%
1.25 5
 
2.8%
1.05 4
 
2.3%
0.75 4
 
2.3%
1.19 4
 
2.3%
Other values (68) 125
70.6%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

OD280/OD315 of diluted wines
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6042938
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:25.024453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.46
Q11.93
median2.78
Q33.17
95-th percentile3.572
Maximum4
Range2.73
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation0.7051029
Coefficient of variation (CV)0.2707463
Kurtosis-1.1039183
Mean2.6042938
Median Absolute Deviation (MAD)0.52
Skewness-0.32042445
Sum460.96
Variance0.4971701
MonotonicityNot monotonic
2023-12-06T23:33:25.132468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
3 4
 
2.3%
2.78 4
 
2.3%
1.82 4
 
2.3%
2.31 3
 
1.7%
2.77 3
 
1.7%
3.17 3
 
1.7%
2.96 3
 
1.7%
3.33 3
 
1.7%
1.56 3
 
1.7%
Other values (111) 142
80.2%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%
3.56 1
0.6%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean745.09605
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-06T23:33:25.242307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.4
Q1500
median672
Q3985
95-th percentile1298
Maximum1680
Range1402
Interquartile range (IQR)485

Descriptive statistics

Standard deviation314.88405
Coefficient of variation (CV)0.42260867
Kurtosis-0.2194105
Mean745.09605
Median Absolute Deviation (MAD)200
Skewness0.78379985
Sum131882
Variance99151.962
MonotonicityNot monotonic
2023-12-06T23:33:25.348454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 5
 
2.8%
680 5
 
2.8%
625 4
 
2.3%
630 4
 
2.3%
750 4
 
2.3%
660 3
 
1.7%
450 3
 
1.7%
510 3
 
1.7%
1035 3
 
1.7%
480 3
 
1.7%
Other values (111) 140
79.1%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Interactions

2023-12-06T23:33:20.634417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.159431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.136802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.031819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.909837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.893675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.697540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.541929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.338382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.158376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.131637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.958158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.780063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.707433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.303524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.211816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.107819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.984150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.960622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.769524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.609227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.408063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.335076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.202879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.027220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.851186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.769148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.371519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.273799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.178178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.049284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.019542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.831829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.667766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.469450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.398462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.266325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.090276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.913901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.837178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.442835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.344803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.247207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.122245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.085564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.899246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.731731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.534573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.472477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.334352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.155656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.982634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.912485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.517977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.419820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.324729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.195582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.155560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.970976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.799241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.602461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.544457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.402325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.228864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.053634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.970481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.581816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.483819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.385243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.259607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.212952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.029987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.856877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.662418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.606476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.462914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.287036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.116044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.146638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.650288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.555795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.452963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.329608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.275947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.095976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.919582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.724462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.673476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.526320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.352279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.182044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.203667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.713693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.627817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.513983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.392672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.333947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.156986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.973476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.784506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.735452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.583575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.410955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.246048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.267537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.779693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.693819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.578610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.458605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.391944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.221987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.033450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.843164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.802564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.644576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.469967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.307176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.337096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.848701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.761819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.646321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.527575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.453946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.287988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.096760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.911188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.870500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.707575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.535941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.372998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.406712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.914819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.827795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.712747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.691585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.513944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.349855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.153677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.972208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.934098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.769161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.595999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.437432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.474387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:10.989797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.892849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.776625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.757852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.571977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.413512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.215312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.033598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.996908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.831076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.654999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.503405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:21.548496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.064820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:11.962821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:12.842593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:13.825673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:14.635984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:15.476957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:16.276336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:17.095597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.067021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:18.895100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:19.716915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T23:33:20.569718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-06T23:33:25.434464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Alcalinity of ashAlcoholAshClassColor intensityFlavanoidsHueMagnesiumMalic acidNonflavanoid phenolsOD280/OD315 of diluted winesProanthocyaninsProlineTotal phenols
Alcalinity of ash1.000-0.2970.3730.378-0.071-0.438-0.352-0.1590.3040.387-0.317-0.245-0.451-0.372
Alcohol-0.2971.0000.2420.5790.6360.286-0.0280.3570.146-0.1580.0890.1830.6300.305
Ash0.3730.2421.0000.2220.2830.076-0.0520.3610.2330.148-0.0120.0220.2510.130
Class0.3780.5790.2221.0000.138-0.855-0.619-0.2410.3480.473-0.742-0.568-0.570-0.726
Color intensity-0.0710.6360.2830.1381.000-0.050-0.4210.3550.2920.062-0.326-0.0370.4560.007
Flavanoids-0.4380.2860.076-0.855-0.0501.0000.5370.225-0.325-0.5440.7400.7290.4230.879
Hue-0.352-0.028-0.052-0.619-0.4210.5371.0000.033-0.561-0.2680.4870.3420.2050.440
Magnesium-0.1590.3570.361-0.2410.3550.2250.0331.0000.085-0.2330.0420.1630.5040.240
Malic acid0.3040.1460.2330.3480.292-0.325-0.5610.0851.0000.255-0.254-0.243-0.055-0.280
Nonflavanoid phenols0.387-0.1580.1480.4730.062-0.544-0.268-0.2330.2551.000-0.494-0.383-0.267-0.447
OD280/OD315 of diluted wines-0.3170.089-0.012-0.742-0.3260.7400.4870.042-0.254-0.4941.0000.5490.2450.687
Proanthocyanins-0.2450.1830.022-0.568-0.0370.7290.3420.163-0.243-0.3830.5491.0000.3020.666
Proline-0.4510.6300.251-0.5700.4560.4230.2050.504-0.055-0.2670.2450.3021.0000.415
Total phenols-0.3720.3050.130-0.7260.0070.8790.4400.240-0.280-0.4470.6870.6660.4151.000

Missing values

2023-12-06T23:33:21.652564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-06T23:33:21.810455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
0113.201.782.1411.21002.652.760.261.284.381.053.401050
1113.162.362.6718.61012.803.240.302.815.681.033.171185
2114.371.952.5016.81133.853.490.242.187.800.863.451480
3113.242.592.8721.01182.802.690.391.824.321.042.93735
4114.201.762.4515.21123.273.390.341.976.751.052.851450
5114.391.872.4514.6962.502.520.301.985.251.023.581290
6114.062.152.6117.61212.602.510.311.255.051.063.581295
7114.831.642.1714.0972.802.980.291.985.201.082.851045
8113.861.352.2716.0982.983.150.221.857.221.013.551045
9114.102.162.3018.01052.953.320.222.385.751.253.171510
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
167313.582.582.6924.51051.550.840.391.548.6600000.741.80750
168313.404.602.8625.01121.980.960.271.118.5000000.671.92630
169312.203.032.3219.0961.250.490.400.735.5000000.661.83510
170312.772.392.2819.5861.390.510.480.649.8999990.571.63470
171314.162.512.4820.0911.680.700.441.249.7000000.621.71660
172313.715.652.4520.5951.680.610.521.067.7000000.641.74740
173313.403.912.4823.01021.800.750.431.417.3000000.701.56750
174313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
175313.172.592.3720.01201.650.680.531.469.3000000.601.62840
176314.134.102.7424.5962.050.760.561.359.2000000.611.60560